High density optical neuroimaging predicts surgeons’s subjective experience and skill levels

Abstract

Measuring cognitive load is important for surgical education and patient safety. Traditional approaches of measuring cognitive load of surgeons utilise behavioural metrics to measure performance and surveys and questionnaires to collect reports of subjective experience. These have disadvantages such as sporadic data, occasionally intrusive methodologies, subjective or misleading self-reporting. In addition, traditional approaches use subjective metrics that cannot distinguish between skill levels. Functional neuroimaging data was collected using a high density, wireless NIRS device from sixteen surgeons (11 attending surgeons and 5 surgery resident) and 17 students while they performed two laparoscopic tasks (Peg transfer and String pass). Participant’s subjective mental load was assessed using the NASA-TLX survey. Machine learning approaches were used for predicting the subjective experience and skill levels. The Prefrontal cortex (PFC) activations were greater in students who reported higher-than-median task load, as measured by the NASA-TLX survey. However in the case of attending surgeons the opposite tendency was observed, namely higher activations in the lower v higher task loaded subjects. We found that response was greater in the left PFC of students particularly near the dorso- and ventrolateral areas. We quantified the ability of PFC activation to predict the differences in skill and task load using machine learning while focussing on the effects of NIRS channel separation distance on the results. Our results showed that the classification of skill level and subjective task load could be predicted based on PFC activation with an accuracy of nearly 90%. Our finding shows that there is sufficient information available in the optical signals to make accurate predictions about the surgeons’ subjective experiences and skill levels. The high accuracy of results is encouraging and suggest the integration of the strategy developed in this study as a promising approach to design automated, more accurate and objective evaluation methods.

Neuromonitoring Correlates of Expertise Level in Surgical Performers: A Systematic Review

“…Notably, the use of questionable ‘experts’ was not limited to the James et al (2011) study. Keles et al (2021) performed expertise level classification using machine learning techniques. They compared PFC activity in novice medical students compared to attending surgeons in laparoscopic peg transfer and string threading tasks.…”

Video game experience affects performance, cognitive load, and brain activity in laparoscopic surgery training

“…A total mental workload score was obtained from the sum of the criteria scores. To analyze the fNIRS data, source-detector pair readings (with a separation of 3.35 cm) at two distinct wavelengths are rst converted into hemoglobin concentration changes via the modi ed Beer-Lambert law, and the average of the task episode are calculated (3). In this paper the standard deviation of the oxyhemoglobin concentration changes over an episode is denoted HbO and represents the extent of local cortical activation of a subject during the corresponding experimental episode.…”

“…After the video session the 15 min training was repeated by each subject for gamers and non-gamers. Further details of the procedure were provided in a recent study where we reported an analysis of the brain activity of surgeons and a subset of the student participants without regard to game playing experience (3). All participants lled out a questionnaire detailing their video game experience including the frequency, duration and category.…”

“…It is critical for residents to reach expertise in a safe training environment with documented criteria. There is increasing interest in characterising not only the observed performance but also the cognitive effort and physiological and brain activity pro les of trainees with the ultimate aim of devising better training and assessment methodologies (1)(2)(3). While direct observation may indicate that a trainee performs adequately, it may fail to predict the long term retention of skill or actual performance beyond the training environment, and trainees who perform identically may differ in subsequent realworld performance.…”

Interhemispheric functional connectivity in the primary motor cortex distinguishes between training on a physical and a virtual surgical simulator

“…While mainly the lateral portion of BA 6 of the frontal cortex showed activation for tasks i-iii, both the lateral and the medial portions of BA 6, including the premotor cortex and the supplementary motor area (SMA), showed activation in the most complex bimanually coordinated task v. In order to investigate these neural correlates (brain aspect) of laparoscopic surgery training, functional near infrared spectroscopy (fNIRS) can provide a better temporal resolution and ease of administration when compared to fMRI; however, at a lower spatial resolution and signal-to-noise ratio (SNR) [2]. Many prior works [3][4][5][6][7] have assessed surgery training using fNIRS mainly to compare skill levels; however, we could not find prior works on portable neuroimaging to compare surgery training in physical versus virtual simulators. This is crucial since surgical training field has seen a rapid emergence of new virtual training technologies which are gradually replacing physical training simulators with virtual training simulators.…”